This dataset provides realistic Internet of Things (IoT) traffic time-series data generated using the novel Tiered Markov-Modulated Stochastic Process (TMMSP) framework. The dataset captures the unique temporal dynamics and stochastic characteristics of three distinct IoT applications: smart city, eHealth, and smart factory systems. Each application's traffic pattern reflects real-world behaviors including human-machine correlation (HMC), sudden data bursts, and application-specific seasonality patterns.

The traffic data is presented as time-series with 1-minute resolution over multiple days, incorporating:

• Daily traffic volume fluctuations reflecting human activity patterns
• Application-specific coordinated transmission phases resulting in data bursts
• Varying traffic intensities based on application characteristics
• Temporal correlation between IoT nodes
• Realistic traffic behavior validated against real IoT application traces

This dataset is particularly valuable for:

• Evaluating resource allocation algorithms for edge/cloud computing
• Testing traffic prediction models
• Analyzing application-specific IoT network behaviors
• Developing and validating network slicing strategies
• Studying autonomous resource scaling mechanisms

The dataset has been validated through comparison with real IoT traffic patterns and demonstrated utility in evaluating autonomous edge slicing (AES) mechanisms. The included traffic patterns exhibit different human-machine correlations and burst frequencies that match expected behaviors of real-world IoT deployments.